Abstract: Smart manufacturing systems based on software become very dependent on embedded control applications, industrial automation software and real-time decision mechanisms to ensure the reliability of production. Nonetheless, software failures that may arise in such systems are usually not foreseeable because datasets are highly unbalanced, in which the number of faulty software modules is much less than the number of non-faulty modules. This paper will present an adaptive hybrid learning paradigm of software fault prediction in smart manufacturing systems through class imbalance optimization. The proposed framework is composed of three significant modules: (1) Feature Extraction Module that is used to extract software metrics, execution logs and operational indicators; (2) Class Imbalance Optimization Module that applies Synthetic Minority Oversampling Technique (SMOTE) and Borderline-SMOTE to equalize minority fault classes; and (3) Hybrid Prediction Module that implements the use of the random forest, support vector machine, Multi-Layer Perceptron and Bayesian Network through weighted voting classification. NASA software defect repositories and PROMISE Repository datasets are benchmark software defect datasets on which models are validated. As shown by the results of the experiment, the proposed framework has a prediction accuracy of 97.1%, precision of 96.2 and a recall of 95.8, which is superior to traditional single classifiers. The framework enhances the detection of minority faults, minimizes false negatives and facilitates predictor software maintenance in industrial automation conditions. Future directions of work are explainable fault prediction and real-time deployment in edge-based manufacturing systems.

Key words: software fault prediction, smart manufacturing, class imbalance, hybrid ensemble learning, predictive maintenance, industrial software reliability